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--- |
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tags: |
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- summarization |
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datasets: |
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- gigaword |
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license: mit |
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thumbnail: https://en.wikipedia.org/wiki/Bart_Simpson#/media/File:Bart_Simpson_200px.png |
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--- |
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# BART for Gigaword |
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- This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept. |
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- The BART Tokenizer for CNN-Dailymail was used in the fine-tuning process and that is the tokenizer that will be loaded automatically when doing: |
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``` |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("a1noack/bart-large-gigaword") |
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``` |
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# Summary generation |
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- This model achieves ROUGE-1 / ROUGE-2 / ROUGE-L of 37.28 / 18.58 / 34.53 on the Gigaword test set; this is pretty good when compared to PEGASUS, `google/pegasus-gigaword`, which achieves 39.12 / 19.86 / 36.24. |
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- To achieve these results, generate text using the code below. `text_list` is a list of input text string. |
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``` |
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input_ids_list = tokenizer(text_list, truncation=True, max_length=128, |
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return_tensors='pt', padding=True)['input_ids'] |
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output_ids_list = model.generate(input_ids_list, min_length=0) |
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outputs_list = tokenizer.batch_decode(output_ids_list, skip_special_tokens=True, |
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clean_up_tokenization_spaces=False) |
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``` |
